Halogen Bonding from Dispersion-Corrected Density-Functional Theory: The Role of Delocalization Error
Bibliographic record
Abstract
Halogen bonds are formed when a Lewis base interacts with a halogen atom in a different molecule, which acts as an electron acceptor. Due to its charge transfer component, halogen bonding is difficult to model using many common density-functional approximations because they spuriously overstabilize halogen-bonded dimers. It has been suggested that dispersion-corrected density functionals are inadequate to describe halogen bonding. In this work, we show that the exchange-hole dipole moment (XDM) dispersion correction coupled with functionals that minimize delocalization error (for instance, BH&HLYP, but also other half-and-half functionals) accurately model halogen-bonded interactions, with average errors similar to other noncovalent dimers with less charge-transfer effects. The performance of XDM is evaluated for three previously proposed benchmarks (XB18 and XB51 by Kozuch and Martin, and the set proposed by Bauzá et al.) spanning a range of binding energies up to ∼50 kcal/mol. The good performance of BH&HLYP-XDM is comparable to M06-2X, and extends to the "extreme" cases in the Bauzá set. This set contains anionic electron donors where charge transfer occurs even at infinite separation, as well as other charge transfer dimers belonging to the pnictogen and chalcogen bonding classes. We also show that functional delocalization error results in an overly delocalized electron density and exact-exchange hole. We propose intermolecular Bader delocalization indices as an indicator of both the donor-acceptor character of an intermolecular interaction and the delocalization error coming from the underlying functional.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".